Tropical Cyclone Morphology from Spaceborne Synthetic Aperture Radar
Bibliographic record
Abstract
In 2008, the Canadian Space Agency sponsored the Radarsat Hurricane Applications Project (RHAP), for researching new developments in the application of Radarsat-1 synthetic aperture radar (SAR) data and innovative mapping approaches to better understand the dynamics of tropical cyclone genesis, morphology, and movement. Although tropical cyclones can be detected by many remote sensors, SAR can yield high-resolution (subkilometer) and low-level storm information that cannot be seen below the clouds by other sensors. In addition to the wind field and tropical cyclone eye information, structures associated with atmospheric processes can also be detected by SAR. We have acquired 161 Radarsat-1 SAR images through RHAP between 2001 and 2007. Among these, 73 images show clear tropical cyclone eye structure. In addition, we also acquired 10 images from the European Space Agency's Envisat SAR between 2004 and 2010. Both Atlantic hurricanes and Pacific typhoons are included. In this study, we analyze these 83 (73 Radarsat-1 and 10 Envisat) images with tropical cyclone eye information along with ancillary tropical cyclone intensity information from the archive to generate tropical cyclone morphology statistics. Histograms of wave-number asymmetry and intensity are presented. The statistics show that when the storm has higher intensity, the tropical cyclone eye tends to become more symmetric, and the area of the tropical cyclone eye, defined by the minimum wind area, tends to be smaller. Examples of finescale structures within the tropical cyclone (i.e., eye/eyewall mesovortices, arc clouds, double eyewalls, and abnormally high wind or rain within eyes) are presented and discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".